University of Pennsylvania

Position Title/Short Description

Full Text of JOE Listing:

Postdoc with background in machine learning or econometrics sought for an interdisciplinary research project developing and testing machine learning methods for inferring causality, including constructing statistically efficient methods for dynamically assigning subjects to "A/B testing" trials with dozens of treatment arms and developing novel methods to optimally exploit population heterogeneity matching treatments to people.

This work will include (a) analyzing a large data set we have collected testing the effects of 57 different interventions designed to encourage people to exercise (b) developing new methods for collecting and analyzing such data and (c) writing papers on this work.

The position is at the University of Pennsylvania, but you will work with a distinguished team of economists and computer scientists including:
Katy Milkman (Wharton, U. Pennsylvania)
Sendhil Mullainathan (Chicago Booth)
Jann Spiess (MSR New England; Stanford GSB (effective July 2019))
Lyle Ungar (CIS U. Pennsylvania)

For further information, contact:
Prof. Lyle Ungar (ungar@upenn.edu)